.TH std::experimental::parallel::transform_reduce 3 "2024.06.10" "http://cppreference.com" "C++ Standard Libary"
.SH NAME
std::experimental::parallel::transform_reduce \- std::experimental::parallel::transform_reduce

.SH Synopsis
   Defined in header <experimental/numeric>
   template< class InputIt, class UnaryOp, class T, class BinaryOp
   >

   T transform_reduce( InputIt first, InputIt last,                \fB(1)\fP (parallelism TS)

                       UnaryOp unary_op, T init, BinaryOp
   binary_op );
   template< class ExecutionPolicy,

             class InputIt, class UnaryOp, class T, class BinaryOp
   >
   T transform_reduce( ExecutionPolicy&& policy,                   \fB(2)\fP (parallelism TS)
                       InputIt first, InputIt last,

                       UnaryOp unary_op, T init, BinaryOp
   binary_op );

   Applies unary_op to each element in the range [first, last) and reduces the results
   (possibly permuted and aggregated in unspecified manner) along with the initial
   value init over binary_op.

   The behavior is non-deterministic if binary_op is not associative or not
   commutative.

   The behavior is undefined if unary_op or binary_op modifies any element or
   invalidates any iterator in [first, last).

.SH Parameters

   first, last - the range of elements to apply the algorithm to
   init        - the initial value of the generalized sum
   policy      - the execution policy
   unary_op    - unary FunctionObject that will be applied to each element of the input
                 range. The return type must be acceptable as input to binary_op
   binary_op   - binary FunctionObject that will be applied in unspecified order to the
                 results of unary_op, the results of other binary_op and init
.SH Type requirements
   -
   InputIt must meet the requirements of LegacyInputIterator.

.SH Return value

   Generalized sum of init and unary_op(*first), unary_op(*(first + 1)), ...
   unary_op(*(last - 1)) over binary_op, where generalized sum GSUM(op, a
   1, ..., a
   N) is defined as follows:

     * if N = 1, a
       1,
     * if N > 1, op(GSUM(op, b
       1, ..., b
       K), GSUM(op, b
       M, ..., b
       N)) where

     * b
       1, ..., b
       N may be any permutation of a1, ..., aN and
     * 1 < K + 1 = M ≤ N

   in other words, the results of unary_op may be grouped and arranged in arbitrary
   order.

.SH Complexity

   O(last - first) applications each of unary_op and binary_op.

.SH Exceptions

     * If execution of a function invoked as part of the algorithm throws an exception,

     * if policy is parallel_vector_execution_policy, std::terminate is called.
     * if policy is sequential_execution_policy or parallel_execution_policy, the
       algorithm exits with an exception_list containing all uncaught exceptions. If
       there was only one uncaught exception, the algorithm may rethrow it without
       wrapping in exception_list. It is unspecified how much work the algorithm will
       perform before returning after the first exception was encountered.
     * if policy is some other type, the behavior is implementation-defined.
     * If the algorithm fails to allocate memory (either for itself or to construct an
       exception_list when handling a user exception), std::bad_alloc is thrown.

.SH Notes

   unary_op is not applied to init.

   If the range is empty, init is returned, unmodified.

     * If policy is an instance of sequential_execution_policy, all operations are
       performed in the calling thread.
     * If policy is an instance of parallel_execution_policy, operations may be
       performed in unspecified number of threads, indeterminately sequenced with each
       other.
     * If policy is an instance of parallel_vector_execution_policy, execution may be
       both parallelized and vectorized: function body boundaries are not respected and
       user code may be overlapped and combined in arbitrary manner (in particular,
       this implies that a user-provided Callable must not acquire a mutex to access a
       shared resource).

.SH Example

   transform_reduce can be used to parallelize std::inner_product:


// Run this code

 #include <boost/iterator/zip_iterator.hpp>
 #include <boost/tuple.hpp>
 #include <experimental/execution_policy>
 #include <experimental/numeric>
 #include <functional>
 #include <iostream>
 #include <iterator>
 #include <vector>

 int main()
 {
     std::vector<double> xvalues(10007, 1.0), yvalues(10007, 1.0);

     double result = std::experimental::parallel::transform_reduce(
         std::experimental::parallel::par,
         boost::iterators::make_zip_iterator(
             boost::make_tuple(std::begin(xvalues), std::begin(yvalues))),
         boost::iterators::make_zip_iterator(
             boost::make_tuple(std::end(xvalues), std::end(yvalues))),
         [](auto r) { return boost::get<0>(r) * boost::get<1>(r); }
         0.0,
         std::plus<>()
     );
     std::cout << result << '\\n';
 }

.SH Output:

 10007

.SH See also

   accumulate       sums up or folds a range of elements
                    \fI(function template)\fP
                    applies a function to a range of elements, storing results in a
   transform        destination range
                    \fI(function template)\fP
   reduce           similar to std::accumulate, except out of order
   (parallelism TS) \fI(function template)\fP
